Individual Tree Diameter At Breast Height Research Articles

Estimation of Diameter at Breast Height in Tropical Forests Based on Terrestrial Laser Scanning and Shape Diameter Function

Estimating forest carbon content typically requires the precise measurement of the trees’ diameter at breast height (DBH), which is crucial for maintaining the health and sustainability of natural forests. Currently, Terrestrial Laser Scanning (TLS) systems are commonly used to acquire forest point cloud data for DBH estimation. However, traditional circular fitting methods face challenges such as a reliance on forest elevation normalization and underfitting of large trees. This study explores a novel approach, the Shape Diameter Function (SDF) algorithm model, leveraging the advantages of three-dimensional point cloud information to replace traditional circular fitting methods. This study employed a parallel approach, combining the Digital Elevation Model (DEM) with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to segment tree point clouds at breast height. Additionally, a point cloud SDF algorithm based on an octree structure was proposed to accurately estimate individual tree DBH. The research data were obtained from tropical secondary forests located in Cameroon, Peru, Indonesia, and Guyana, with forest ground point cloud data acquired via TLS. The experimental results demonstrated the superior performance of the SDF algorithm in estimating DBH. Compared with the Random Sample Consensus (RANSAC) and Hough transform methods, the Root Mean Square Error (RMSE) decreased by 28.1% and 47.8%, respectively. Particularly in estimating DBH for large trees, the SDF algorithm exhibited smaller errors, indicating a closer alignment between the estimated individual tree DBH values and those obtained from manual measurements. This study presented a more accurate DBH estimation algorithm, contributing to the exploration of improved forest carbon content estimation methods.

The Minimum Target Diameter and the Harvest Age of Oak Natural Secondary Forests in Different Sites Conditions: Case Study in Hunan Province, China

Maintaining permanent forest canopy cover and eventually harvesting timber by predetermined target diameter are often considered as a prototype for future management of the oak natural forest. However, target diameters and harvest age based on average forest growth rates from wide geographical areas often hamper improved management of oak forests. In this study, based on the sampling of 129 target trees from 51 oak natural secondary forest plots in Hunan Province, China, an individual-tree DBH (diameter at breast height) growth model of oak target trees was developed, and the site type (41 levels) was related to the model as random effects by a nonlinear mixed-effects approach. Moreover, the 41 site types were clustered into four site type groups (STG1, STG2, STG3, and STG4) by the K-means clustering algorithm to improve the model performance and practicality. With the help of the model, the five target diameters (including 24, 30, 40, 50, and 60 cm) were simulated in each of the four STGs, and the minimum target diameter was determined for each STG based on the theory of quantitative maturity. In the four STGs, the harvest age of the 24 cm diameter target ranged from 30 to 51 years; the harvest age of the 60 cm target diameter ranged from 131 to 220 years, with the oaks failing to reach 60 cm in the lowest-quality STG4; the minimum target diameter ranged from 21 cm to 29 cm. Results showed that lower-quality sites exclude higher target diameters from optimal harvesting strategies, in contrast to the higher target diameter as a more reasonable strategy in higher quality sites, and that the minimum target diameter is significantly influenced by site conditions. Therefore, it is necessary to develop a diverse target-diameter-harvesting strategy adapted for the complex site conditions of oak forests in Hunan Province towards site-specific timber management to improve the sustainability of timber production in oak forests.

DBH Estimation for Individual Tree: Two-Dimensional Images or Three-Dimensional Point Clouds?

Accurate forest parameters are crucial for ecological protection, forest resource management and sustainable development. The rapid development of remote sensing can retrieve parameters such as the leaf area index, cluster index, diameter at breast height (DBH) and tree height at different scales (e.g., plots and stands). Although some LiDAR satellites such as GEDI and ICESAT-2 can measure the average tree height in a certain area, there is still a lack of effective means for obtaining individual tree parameters using high-resolution satellite data, especially DBH. The objective of this study is to explore the capability of 2D image-based features (texture and spectrum) in estimating the DBH of individual tree. Firstly, we acquired unmanned aerial vehicle (UAV) LiDAR point cloud data and UAV RGB imagery, from which digital aerial photography (DAP) point cloud data were generated using the structure-from-motion (SfM) method. Next, we performed individual tree segmentation and extracted the individual tree crown boundaries using the DAP and LiDAR point cloud data, respectively. Subsequently, the eight 2D image-based textural and spectral metrics and 3D point-cloud-based metrics (tree height and crown diameters) were extracted from the tree crown boundaries of each tree. Then, the correlation coefficients between each metric and the reference DBH were calculated. Finally, the capabilities of these metrics and different models, including multiple linear regression (MLR), random forest (RF) and support vector machine (SVM), in the DBH estimation were quantitatively evaluated and compared. The results showed that: (1) The 2D image-based textural metrics had the strongest correlation with the DBH. Among them, the highest correlation coefficient of −0.582 was observed between dissimilarity, variance and DBH. When using textural metrics alone, the estimated DBH accuracy was the highest, with a RMSE of only 0.032 and RMSE% of 16.879% using the MLR model; (2) Simply feeding multi-features, such as textural, spectral and structural metrics, into the machine learning models could not have led to optimal results in individual tree DBH estimations; on the contrary, it could even reduce the accuracy. In general, this study indicated that the 2D image-based textural metrics have great potential in individual tree DBH estimations, which could help improve the capability to efficiently and meticulously monitor and manage forests on a large scale.

Thinning Levels of Laurel Natural Regeneration to Establish Traditional Agroforestry Systems, Ecuadorian Amazon Upper Basin

(1) Background: The Cordia alliodora (Ruiz & Pav.) Oken (laurel) natural regeneration management is a widespread practice among smallholders in the Amazon upper basin for the establishment of traditional agroforestry systems. This tree management approach is opposite to the development project proposals that contemplate reforestation with nursery seedlings in the Amazon region. The present study evaluated the effects of thinning levels on the diameter and basal area increment of laurel in a traditional agroforestry system; (2) Methods: A randomized complete block design with three replications was used, and the target variables were the growth rate of diameter at breast height (DBH) and the basal area of trees. Twelve square field plots, 400 m² each, were established in a plot network covering a 1.0 km × 0.5 km area. Three thinning levels were applied: light, moderate, and intensive thinning; (3) Results: The traditional agroforestry system investigated was characterized by a marginal growth of laurel trees, with an average yield of 125.26 m3 ha−1 (±15.39) and MAI of 13.92 m3 ha−1 at 9 years of age with a tree density of 418 trees ha−1. The average value of the relation between the number of trees and the basal area removed (NG value) was 1.15, with small variation among plots, so all of them were thinned from below. The intensive thinning treatment, leaving 200 trees ha−1, caused the greatest annual increase in individual tree DBH (2.03 cm) and basal area (61.37 cm2) in both absolute and relative terms and improved tree height/diameter ratio; (4) Conclusions: Management of laurel natural regeneration for the establishment of traditional agroforestry systems could be improved by thinning at early ages, leaving 200 well-spaced laurel trees per ha.

An improved area-based approach for estimating plot-level tree DBH from airborne LiDAR data

The diameter at breast height (DBH) of trees and stands is not only a widely used plant functional trait in ecology and biodiversity but also one of the most fundamental measurements in managing forests. However, systematically measuring the DBH of individual trees over large areas using conventional ground-based approaches is labour-intensive and costly. Here, we present an improved area-based approach to estimate plot-level tree DBH from airborne LiDAR data using the relationship between tree height and DBH, which is widely available for most forest types and many individual tree species. We first determined optimal functional forms for modelling height-DBH relationships using field-measured tree height and DBH. Then we estimated plot-level mean DBH by inverting the height-DBH relationships using the tree height predicted by LiDAR. Finally, we compared the predictive performance of our approach with a classical area-based method of DBH. The results showed that our approach significantly improved the prediction accuracy of tree DBH (R2 ​= ​0.85–0.90, rRMSE ​= ​9.57%–11.26%) compared to the classical area-based approach (R2 ​= ​0.80–0.83, rRMSE ​= ​11.98%–14.97%). Our study demonstrates the potential of using height-DBH relationships to improve the estimation of the plot-level DBH from airborne LiDAR data.

A handheld device for measuring the diameter at breast height of individual trees using laser ranging and deep-learning based image recognition

BackgroundAccurate and efficient measurement of the diameter at breast height (DBH) of individual trees is essential for forest inventories, ecological management, and carbon budget estimation. However, traditional diameter tapes are still the most widely used dendrometers in forest surveys, which makes DBH measurement time-consuming and labor-intensive. Automatic and easy-to-use devices for measuring DBH are highly anticipated in forest surveys. In this study, we present a handheld device for measuring the DBH of individual trees that uses digital cameras and laser ranging, allowing for an instant, automated, and contactless measurement of DBH.ResultsThe base hardware of this device is a digital camera and a laser rangefinder, which are used to take a picture of the targeted tree trunk and record the horizontal distance between the digital camera and the targeted tree, respectively. The core software is composed of lightweight convolutional neural networks (CNNs), which includes an attention-focused mechanism for detecting the tree trunk to log the number of pixels between the edges. We also calibrated the digital camera to correct the distortion introduced by the lens system, and obtained the normalized focal length. Parameters including the horizontal distance between the digital camera and the targeted tree, number of pixels between the edges of the tree trunk, and normalized focal length were used to calculate the DBH based on the principles of geometrical optics. The measured diameter values, and the longitudes and latitudes of the measurement sites, were recorded in a text file, which is convenient to export to external flash disks. The field measurement accuracy test showed that the BIAS of the newly developed device was − 1.78 mm, and no significant differences were found between the measured diameter values and the true values (measured by the conventional tape). Furthermore, compared with most other image-based instruments, our device showed higher measurement accuracy.ConclusionsThe newly developed handheld device realized efficient, accurate, instant, and non-contact measurements of DBH, and the CNNs were proven to be successful in the detection of the tree trunk in our research. We believe that the newly developed device can fulfill the precision requirement in forest surveys, and that the application of this device can improve the efficiency of DBH measurements in forest surveys.

Comparison of Backpack, Handheld, Under-Canopy UAV, and Above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests

In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height and stem volume, we utilized all of the six systems to collect point cloud data on two 32 m-by-32 m test sites classified as sparse (n = 42 trees) and obstructed (n = 43 trees). To analyze the data collected with the two ground-based MLS systems and the under-canopy UAV system, we used a workflow based on our recent work featuring simultaneous localization and mapping (SLAM) technology, a stem arc detection algorithm, and an iterative arc matching algorithm. This workflow enabled us to obtain accurate stem diameter estimates from the point cloud data despite a small but relevant time-dependent drift in the SLAM-corrected trajectory of the scanner. We found out that the ground-based MLS systems and the under-canopy UAV system could be used to measure the stem diameter (DBH) with a root mean square error (RMSE) of 2–8%, whereas the stem curve measurements had an RMSE of 2–15% that depended on the system and the measurement height. Furthermore, the backpack and handheld scanners could be employed for sufficiently accurate tree height measurements (RMSE = 2–10%) in order to estimate the stem volumes of individual trees with an RMSE of approximately 10%. A similar accuracy was obtained when combining stem curves estimated with the under-canopy UAV system and tree heights extracted with an above-canopy flying laser scanning unit. Importantly, the volume estimation error of these three MLS systems was found to be of the same level as the error corresponding to manual field measurements on the two test sites. To analyze point cloud data collected with the three above-canopy flying UAV systems, we used a random forest model trained on field reference data collected from nearby plots. Using the random forest model, we were able to estimate the DBH of individual trees with an RMSE of 10–20%, the tree height with an RMSE of 2–8%, and the stem volume with an RMSE of 20–50%. Our results indicate that ground-based and under-canopy MLS systems provide a promising approach for field reference data collection at the individual tree level, whereas the accuracy of above-canopy UAV laser scanning systems is not yet sufficient for predicting stem attributes of individual trees for field reference data with a high accuracy.

Application of UAV Photogrammetry with LiDAR Data to Facilitate the Estimation of Tree Locations and DBH Values for High-Value Timber Species in Northern Japanese Mixed-Wood Forests

High-value timber species play an important economic role in forest management. The individual tree information for such species is necessary for practical forest management and for conservation purposes. Digital aerial photogrammetry derived from an unmanned aerial vehicle (UAV-DAP) can provide fine spatial and spectral information, as well as information on the three-dimensional (3D) structure of a forest canopy. Light detection and ranging (LiDAR) data enable area-wide 3D tree mapping and provide accurate forest floor terrain information. In this study, we evaluated the potential use of UAV-DAP and LiDAR data for the estimation of individual tree location and diameter at breast height (DBH) values of large-size high-value timber species in northern Japanese mixed-wood forests. We performed multiresolution segmentation of UAV-DAP orthophotographs to derive individual tree crown. We used object-based image analysis and random forest algorithm to classify the forest canopy into five categories: three high-value timber species, other broadleaf species, and conifer species. The UAV-DAP technique produced overall accuracy values of 73% and 63% for classification of the forest canopy in two forest management sub-compartments. In addition, we estimated individual tree DBH Values of high-value timber species through field survey, LiDAR, and UAV-DAP data. The results indicated that UAV-DAP can predict individual tree DBH Values, with comparable accuracy to DBH prediction using field and LiDAR data. The results of this study are useful for forest managers when searching for high-value timber trees and estimating tree size in large mixed-wood forests and can be applied in single-tree management systems for high-value timber species.

Long observation period improves growth prediction in old Sugi (Cryptomeria japonica) forest plantations

ABSTRACT It is important to predict the growth of Sugi forest plantations in old age. When predictions about the growth of Sugi forest plantations are made and there is a lack of growth data from older trees, it is possible that the accuracy of these predictions becomes worse. For example, it is known that the growth of Sugi does not get slower at older ages as expected from past growth predictions based on growth data from young to middle-aged trees. This study investigated the changes in extrapolated values of diameter at breast height (DBH) in old Sugi forest plantations with changes in the observation period of training data for model calibration. The study sites were long-term growth observation sites of Sugi forest plantations in the University of Tokyo Chiba Forest and Chichibu Forest. In this study, both DBH of individual trees and mean DBH of stands were analyzed by fitting Richards growth functions. The results showed that the accuracy of growth predictions in old ages was improved by including growth data from a sufficient number of older trees. From another point of view, growth prediction in old ages tended to underestimate actual growth if growth data did not include enough older trees.

Prediction of Individual Tree Diameter Using a Nonlinear Mixed-Effects Modeling Approach and Airborne LiDAR Data

Rapidly advancing airborne laser scanning technology has become greatly useful to estimate tree- and stand-level variables at a large scale using high spatial resolution data. Compared with that of ground measurements, the accuracy of the inferred information of diameter at breast height (DBH) from a remotely sensed database and the models developed with traditional regression approaches (e.g., ordinary least square regression) may not be sufficient. Thus, this regression approach is no longer appropriate to develop accurate models and predict DBH from remotely sensed-related variables because DBH is subject to the random effects of forest stands. This study developed a generalized nonlinear mixed-effects DBH estimation model from remotely sensed imagery data. The light detection and ranging (LiDAR)-derived stand canopy density, crown projection area, and tree height were used as predictors in the DBH estimation model. These variables can be more readily measured over an extensive forest area with higher accuracy compared to the conventional field-based methods. The airborne LiDAR data for a total of 402 Picea crassifolia Kom trees on a sample plot that were divided into 16 sub-sample plots and located in the most important distribution region of western China were used. The leave-one sub-sample plot-out cross-validation method was applied to evaluate the model’s prediction accuracy. The results indicated that the random effects of the sub-sample plot on the prediction of DBH were large and their inclusion into the DBH model significantly improved the prediction accuracy. The prediction accuracy of the proposed model at the mean (M) response was also substantially improved relative to the accuracy obtained from the base model. Among several tree selection alternatives evaluated, a sample size of the two largest trees per sub-sample plot used in estimating the random effects showed a significantly higher accuracy compared to other sampling alternatives. This sample size would balance both the measurement cost and potential prediction errors. The nonlinear mixed-effects DBH estimation model at the M response can also be applied if obtaining the estimates of individual tree DBH with a relatively lower cost, and a lower prediction accuracy was the purpose of the study.

Generalized Nonlinear Mixed-Effects Individual Tree Diameter Increment Models for Beech Forests in Slovakia

Individual tree growth and yield models precisely describe tree growth irrespective of stand complexity and are capable of simulating various silvicultural alternatives in the stands with diverse structure, species composition, and management history. We developed both age dependent and age independent diameter increment models using long-term research sample plot data collected from both monospecific and mixed stands of European beech (Fagus sylvatica L.) in the Slovak Republic. We used diameter at breast height (DBH) as a main predictor and other characteristics describing site quality (site index), stand development stage (dominant height and stand age), stand density or competition (ratio of individual tree DBH to quadratic mean diameter), species mixture (basal area proportion of a species of interest), and dummy variable describing stand management regimes as covariate predictors to develop the models. We evaluated eight versatile growth functions in the first stage using DBH as a single predictor and selected the most suitable one, i.e., Chapman-Richards function for further analysis through the inclusion of covariate predictors. We introduced the random components describing sample plot-level random effects and stochastic variations on the diameter increment, into the models through the mixed-effects modelling. The autocorrelation caused by hierarchical data-structure, which is assumed to be partially reduced by mixed-effects modelling, was removed through the inclusion of the parameter accounting for the autoregressive error-structures. The models described about two-third parts of a total variation in the diameter increment without significant trends in the residuals. Compared to the age independent mixed-effects model (conditional coefficient of determination, R c 2 = 0.6566; root mean square error, RMSE = 0.1196), the age dependent model described a significantly larger proportion of the variations in diameter increment ( R c 2 = 0.6796, RMSE = 0.1141). Diameter increment was significantly influenced differently by covariate predictors included into the models. Diameter increment decreased with the advancement of stand development stage (increased dominant height and stand age), increasing intraspecific competition (increased basal area proportion of European beech per sample plot), and diameter increment increased with increasing site quality (increased site index) and decreased competition (increased ratio of DBH to quadratic mean diameter). Our mixed-effects models, which can be easily localized with the random effects estimated from prior measurement of diameter increments of four randomly selected trees per sample plot, will provide high prediction accuracies. Our models may be used for simulating growth of European beech irrespective of its stand structural complexity, as these models have included various covariate variables describing both tree-and stand-level characteristics, thinning regimes, except the climate characteristics. Together with other forest models, our models will be used as inputs to the growth simulator to be developed in the future, which is important for decision-making in forestry.

Influence of shaded systems on Xylosandrus compactus infestation in Robusta coffee along a rainfall gradient in Uganda

Abstract We investigated the relationship between characteristics of coffee shade systems and coffee pest infestation by the black coffee twig borer Xylosandrus compactus Eichhoff. The pest deprives Uganda of $40 millions annually, yet its control remains inadequate. The present study considered three rainfall zones in Central Uganda and 50 coffee plots that were randomly selected from each rainfall zone. Data were collected on X. compactus infestation and key shade indicators: canopy cover, tree‐species densities, diameter at breast height (DBH) and ratio of coffee to banana. Cluster analysis revealed two coffee shade systems: a matured shade tree (MST) system and a young poly‐culture (YPC) system. Xylosandrus compactus infestations were significantly less in the MST system than in the YPC system and significantly less in the low rainfall zone than in the high rainfall zone. An increase in the density of Carica papaya and Albizia chinensis significantly reduced and increased X. compactus infestation, respectively. A higher average DBH of individual trees and a higher density of trees that exude sap significantly lowered X. compactus infestation. Suppressing X. compactus infestation requires bigger trees, a high density of sap‐exuding trees and no Albizia chinensis. Further research should aim to investigate X. compactus flight activity and microclimate influencing X. compactus population dynamics.

Competition among dominant tree species in a natural spruce-fir forest in Changbai Mountain

Dominant tree species are the main species in different layers of mixed-species forests. Examining competitive relationships among dominant tree species is important for proper management of mixedwood forests. In this study, we established a 100 m×100 m plot of uniform condition on Jingouling Forest Farm, Wangqing County, Jilin Province. The dominant tree species of the young forest community were first determined through dominance index analysis approach and the competitive relationships among dominant tree species were then examined using tree competition index that described intra- and inter-specific competition from the above and sides of individual tree crown. The results indicated three dominant tree species in this mixed-species community were Abies nephrolepis, Picea koraiensis and Pinus koraiensis. A. nephrolepis, Picea koraiensis, Pinus koraiensis, Betula costata, Tilia amurensis, Acer tegmentosum and Betula platyphylla were the main competitors of the dominant tree species, while A. nephrolepis was the major competitor for all three dominant tree species (1412.48), followed by Picea koraiensis (439.17) and Pinus koraiensis (245.28), they all had side squeeze effect (64.9%, 65.2% and 66.0% of the total competition for the three dominant tree species, respectively). The level of side and above competition decreased with individual tree DBH (diameter at breast height); the percentage of side competition was nearly equal to that of above competition in small trees and increased with tree size, reaching nearly 100% in large canopy trees. For the three dominant tree species, the side and above competition of three dominant tree species mainly came from A. nephrolepis, Picea koraiensis, Pinus koraiensis, T. amurensis, B. costata, A. tegmentosum and B. platyphylla, and the inter-specific competition was more intense than intra-specific competition (58.4%, 87.1%, 83.7% of the total competition for the three dominant species), both of which decreased with the increase of individual tree DBH.

基于高分辨率遥感影像的森林地上生物量估算

PDF HTML阅读 XML下载 导出引用 引用提醒 基于高分辨率遥感影像的森林地上生物量估算 DOI: 10.5846/stxb201212211841 作者: 作者单位: 南京大学国际地球系统科学研究所,南京大学国际地球系统科学研究所,南京大学国际地球系统科学研究所,南京大学国际地球系统科学研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家"863"高技术研究发展计划项目(2012AA12A306) Estimation of forest aboveground biomass using high spatial resolution remote sensing imagery Author: Affiliation: International Institute for Earth System Science, Nanjing University,International Institute for Earth System Science, Nanjing University,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:以南京市紫金山林区为研究区,利用e-Cognition面向对象分类方法,基于光谱和空间信息融合后的IKONOS影像提取单木树冠阳性冠幅(PoCA)信息,并结合野外实测的样方生物量数据,分别建立了针叶林和阔叶林地上生物量 (AGB)的遥感估算模型,并利用实测森林生物量数据对模型进行了验证。结果表明,基于遥感影像提取的PoCA与实测AGB存在较好的非线性相关关系,所建针叶林AGB估算模型的可靠性优于阔叶林模型。对建模样本而言,估算的针叶林和阔叶林AGB与观测数据比较的R2分别为0.62 (P < 0.01,n=9) 和0.56(P < 0.01,n=16)。验证表明,所建AGB估算模型的可靠性较好,估算的针叶林和阔叶林AGB与观测数据比较的R2分别为0.55(P < 0.01,n=6) 和0.52(P < 0.01,n=10),但当AGB较低时,模型结果偏高,AGB较低时,模型结果偏低。研究说明通过高分辨率遥感数据的融合、提取树冠信息进行生物量估算是可行性的。 Abstract:Forests are important terrestrial ecosystems and play an important role in maintaining ecological environment. Globally, they are acting as a carbon sink and a crucial player in alleviating global climate change. Forest AGB (Aboveground Biomass) is an important indicator of forest functioning and a key component of the carbon cycle in forest ecosystems. The accurate estimation of AGB is of importance for study the budget of global terrestrial ecosystems. However, it is challengeable to map AGB at regional and global scales. In recent years, various studies have been conducted to retrieve AGB using remote sensing data at regional scales due to the more effectiveness and lower cost of remote sensing compared with the traditional inventory method. This study took Zijin Mountain, located in the urban area of Nanjing city, as the study area to explore the applicability of high resolution remote sensing data in retrieving AGB. This study area is dominantly covered by various species of trees. The spectral and spatial information in an IKONOS image were first fused using the Brovey transformation method to generate images of green, red, near infrared bands at a spatial resolution of 1m. Then, positive crown area (PoCA) of individual trees was delineated using the reflectance of green, red, near infrared bands and normalized difference index retrieved from the fused images and the object-oriented classification method implemented in the e-Cognition platform. The classification rules were determined using the See 5 software. Meanwhile, field campaigns were conducted to record the number of trees, the height and diameter at breast height (DBH) of individual trees, tree species, geological coordinates, and topography at 41 representative plots (16 plots for coniferous forests and 25 plots for broadleaf forests) with an area of 25 m×25m. For each plot, AGB was calculated using models developed in previous studies and field measured height and DBH of individual trees. These field samples were randomly selected for developing models (25 samples) and validating the developed modes (16 samples). The retrieved PoCA data were used in conjunction with plot-level AGB measured to develop empirical models for estimating AGB. The developed models were further validated using measured AGB. The results show that PoCA retrieved from remote sensing data shows distinguishable spatial patterns and is tightly correlated with AGB. The developed empirical model is more applicable for coniferous forests than for broadleaf forests in retrieving AGB. As to the samples used to develop the models, the R2 values of estimated AGB against field measurements are 0.62 (P < 0.01,n=9) for coniferous forests and 0.56 (P < 0.01,n=16) for broadleaf forests, respectively. The validation indicates that the developed models are reliable. The R2 values of estimated AGB against measurements are 0.55 (P < 0.01,n=6) for coniferous forests and 0.52 (P < 0.01,n=10) for broadleaf forests, respectively. However, the models tend to overestimate AGB under the condition of low AGB and to underestimate AGB when AGB is high. This study indicates that it is practically feasible to estimate forest AGB using the tree canopy area information retrieved from fused high resolution remote sensing image. 参考文献 相似文献 引证文献

Modeling individual tree growth using temporary plot records: evaluation of a non-parametric diameter distribution based method

We hypothesize that the change, over time, in deciles of a diameter at breast height (DBH) distribution represents a suitable proxy for repeated measurements of individual tree DBH. Based on that hypothesis a method is developed for modeling individual tree DBH growth from temporary plot data. The method assumes evenly distributed or no mortality and no reproduction that would be measured as ingrowth. The temporary plot based DBH projection results generally indicated reduced accuracy and greater bias than permanent plot based DBH projection results. However, some temporary plot based projection results were comparable to the permanent plot based results in terms of accuracy and bias measures. Based on the assumptions and results, the methodology is likely useful only for uniform, single-species plantations.

Biomass and productivity of a Rhizophora mucronata Lamarck plantation in Tritih, Central Java, Indonesia

Perum Perhutani (The State Forestry Corporation) is establishing large-scale mangrove plantations for reforestation and/or afforestation, and the production of wood for fuel as well as other purposes in Java. The mangrove species used currently in Segara Anakan Cilacap is Rhizophora mucronata Lamarck. The above-ground biomass, litter production and litter accumulation in a 7-year-old stand in Tritih were studied. The mean diameter at breast height (DBH) or 30 cm above the highest prop root was 5.91 cm. The mean annual increment in DBH of individual trees was 0.89 cm. The total above-ground biomass of the stand was 93.726 t ha −1 of dry weight, consisting of 60.442 t of stem, 13.906 t of branch, 14.708 t of prop root and 4.670 t of leaf material. Litter production ranged from 7.058 to 10.395 t ha −1 year −1 of dry weight with leaf litter making up 73.29–84.30% of the total. Leaf litter accumulation amounted to 16.922–20.511 t ha −1 year −1 of dry weight and the turnover constant ( k) of leaf litter was estimated at 0.2943–0.4082. The high productivity is discussed in relation to lower turnover ( k) of litter.